This paper presents GridNet, a new Convolutional Neural Network (CNN)
architecture for semantic image segmentation (full scene labelling). Classical
neural networks are implemented as one stream from the input to the output with
subsampling operators applied in the stream in order to reduce the feature maps
size and to increase the receptive field for the final prediction. However, for
semantic image segmentation, where the task consists in providing a semantic
class to each pixel of an image, feature maps reduction is harmful because it
leads to a resolution loss in the output prediction. To tackle this problem,
our GridNet follows a grid pattern allowing multiple interconnected streams to
work at different resolutions. We show that our network generalizes many well
known networks such as conv-deconv, residual or U-Net networks. GridNet is
trained from scratch and achieves competitive results on the Cityscapes
dataset.

GridNet 是一种新的卷积神经网络结构，用于实现语义图像分割任务。该网络采用网格模式使得多个互相连接的流可以在不同的分辨率下工作，以解决特征图降维带来的分辨率丢失。与传统的神经网络相比，GridNet 在 Cityscapes 数据集上表现出了更好的性能。